title: Predicting lymph node output efficiency using systems biology.
abstract: Dendritic cells (DCs) capture pathogens and foreign antigen (Ag) in peripheral tissues and migrate to secondary lymphoid tissues, such as lymph nodes (LNs), where they present processed Ag as MHC-bound peptide (pMHC) to naïve T cells. Interactions between DCs and T cells result, over periods of hours, in activation, clonal expansion and differentiation of antigen-specific T cells, leading to primed cells that can now participate in immune responses. Two-photon microscopy (2PM) has been widely adopted to analyze lymphocyte dynamics and can serve as a powerful in vivo assay for cell trafficking and activation over short length and time scales. Linking biological phenomena between vastly different spatiotemporal scales can be achieved using a systems biology approach. We developed a 3D agent-based cellular model of a LN that allows for the simultaneous in silico simulation of T cell trafficking, activation and production of effector cells under different antigen (Ag) conditions. The model anatomy is based on in situ analysis of LN sections (from primates and mice) and cell dynamics based on quantitative measurements from 2PM imaging of mice. Our simulations make three important predictions. First, T cell encounters by DCs and T cell receptor (TCR) repertoire scanning are more efficient in a 3D model compared with 2D, suggesting that a 3D model is needed to analyze LN function. Second, LNs are able to produce primed CD4+T cells at the same efficiency over broad ranges of cognate frequencies (from 10(-5) to 10(-2)). Third, reducing the time that naïve T cells are required to bind DCs before becoming activated will increase the rate at which effector cells are produced. This 3D model provides a robust platform to study how T cell trafficking and activation dynamics relate to the efficiency of T cell priming and clonal expansion. We envision that this systems biology approach will provide novel insights for guiding vaccine development and understanding immune responses to infection.
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Chang
authors.initials: C
authors.lastname: Gong
authors.forename: Joshua T
authors.initials: JT
authors.lastname: Mattila
authors.forename: Mark
authors.initials: M
authors.lastname: Miller
authors.forename: JoAnne L
authors.initials: JL
authors.lastname: Flynn
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Linderman
authors.forename: D
authors.initials: D
authors.lastname: Kirschner
mesh_terms: D000818:Animals; D000941:Antigens / Q000276:immunology; D015496:CD4-Positive T-Lymphocytes / Q000166:cytology / Q000276:immunology*; D002465:Cell Movement / Q000276:immunology*; D003713:Dendritic Cells / Q000166:cytology / Q000276:immunology*; D008198:Lymph Nodes / Q000166:cytology / Q000276:immunology*; D008213:Lymphocyte Activation / Q000502:physiology*; D051379:Mice; D018448:Models, Immunological; D011948:Receptors, Antigen, T-Cell / Q000276:immunology
chemical_list: D000941:Antigens; D011948:Receptors, Antigen, T-Cell
keywords: 3D; Agent based model; Effector; Priming; T cells